Software engineering scope expands beyond executable code to semi-executable artifacts best diagnosed by the new six-ring Semi-Executable Stack model.
Software engineering for AI-based systems: A survey.ACM Transactions on Software Engineering and Methodology, 31(2)
6 Pith papers cite this work, alongside 58 external citations. Polarity classification is still indexing.
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UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
A code-owned harness enforces source, routing, trace, hygiene, and recommendation contracts for enterprise LLM agents; prompt-only fails and bolt-on guardrails over-refuse.
ML-specific code smells occur 41-94 times less often than general Python smells in 279 projects, with associations to commit frequency and domain but none for general smells or most other project characteristics.
A concept-based pruning method for DNNs guided by interpretable concepts and system requirements produces smaller, computationally efficient models that maintain effectiveness on image classification tasks.
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.
citing papers explorer
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The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE
Software engineering scope expands beyond executable code to semi-executable artifacts best diagnosed by the new six-ring Semi-Executable Stack model.
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UntrustVul: An Automated Approach for Identifying Untrustworthy Alerts in Vulnerability Detection Models
UntrustVul identifies untrustworthy vulnerability predictions by marking lines that neither match historical vulnerability patterns nor influence vulnerable lines through dependencies, reporting AUC 70-88% and F1 82-94% on 115K predictions.
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From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
A code-owned harness enforces source, routing, trace, hygiene, and recommendation contracts for enterprise LLM agents; prompt-only fails and bolt-on guardrails over-refuse.
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Comparing ML-Specific and General Python Code Smells Across Project Characteristics
ML-specific code smells occur 41-94 times less often than general Python smells in 279 projects, with associations to commit frequency and domain but none for general smells or most other project characteristics.
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Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach
A concept-based pruning method for DNNs guided by interpretable concepts and system requirements produces smaller, computationally efficient models that maintain effectiveness on image classification tasks.
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Industry Practitioners Perspectives on AI Model Quality: Perceptions, Challenges, and Solutions
Industry AI practitioners view model quality through nine attributes with context-dependent priorities, where data imbalance is a key challenge addressed by strategies like active learning, as confirmed by interviews and a follow-up survey.